Multi-frequency high-precision object recognition method

ABSTRACT

A multi-frequency high-precision object recognition method is disclosed, wherein a multi-frequency light emitting unit is used to emits lights of different frequencies onto an object-to-be-tested, and a multi-frequency image sensor unit is used to fetch the image of lights reflected from the object-to-be-tested. In an X axis and a Y axis is a single-piece planar image, while lights of different frequencies is used to form image depth in a Z axis. The sample light in the Z axis includes two infrared light narrow range image signals, each having wavelength between 850 nm and 1050 nm, and wavelength width between 10 nm and 60 nm. Calculate to obtain a plurality of single-piece planar images in the X axis and the Y axis as sampled by different wavelength widths in the Z axis, superimpose the plurality of single-piece planar images into a 3-dimension stereoscopic relief image for precise comparison and recognition.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to an image recognition technology, and inparticular to a multi-frequency high-precision object recognitionmethod, that is simple in operation, and is capable of producinghigh-quality, 3-dimension stereoscopic relief images throughsuperimposing images, for precise comparison and recognition.

The Prior Arts

In general, image sensor is mainly used to obtain planar images throughphotoelectrical conversion. As such, image sensors are used extensivelyin the various products of security monitoring, industrial monitoring,face recognition, webcam, drone, robot, and vehicle backup auxiliaryimage fetching.

In particular, in the application of face recognition, it has theadvantages of nature, convenience, and non-contact, such that theobjective of recognition is realized without affecting and disturbingthe normal activities of the people involved. In this age of Internetand intelligent mobile devices, face recognition is getting moreimportant. In this sphere, it has made great progress in recent years,and it has been used in the sphere of identity recognition, datasecurity control, financial payment, medical applications, and visualmonitoring.

Presently, a more advanced face recognition technology is developedthrough using the following two 3-dimension stereoscopic image sensingtechnologies:

1. The Time of Flight (TOF) approach, wherein, infrared light isirradiated on to the surface of an object and is reflected back. Sincethe speed of light (v) is known, and infrared image sensor can be usedto measure the time of reflection (t) from the different positions ofdifferent depths on the surface of the object-to-be-tested. Then, asimple mathematic formula is used to calculate the distances and depthsto the different positions on the surface of the object-to-be-tested.

2. The Structured Light approach: wherein different light patterns areproduced by special light sources, to incident onto the surface of anobject. The distorted light patterns produced by reflections fromdifferent depths at different positions on the surface of an object areused for recognition. For example, the most advanced iPhone Xintelligent handset makes use of a Dot Projector. A high power verticalresonance cavity surface-emitting laser is used to emit infrared laserlight. Then, the laser light travels through the Wafer Level Optics(WLO), the Diffractive Optical Elements (DOE), to produce about 30thousand Structured Light spots, to be projected onto the face of auser. Subsequently, the array formed by the light spots is reflectedback to an Infrared camera, to measure the distances to the depths ofthe different positions on the face of the user.

Refer to FIG. 10, in order to combine effectively the two approachesmentioned above to raise precision of face recognition, the mostadvanced iPhone X intelligent handset is utilized. In this respect, itbasically requires the following devices to achieve face recognition: aninfrared lens a1, a seven-million-pixel lens a2, a flood illuminator a3,a proximity sensor a4, an ambient light sensor a5, and a dot projectora6. The disadvantages of this design are that it requires to use quite alot of devices to cause high cost, while it occupies a rather largespace.

More seriously, even it can afford to use the high price precisionelements mentioned above to produce 30 thousand structured light spots,to be projected onto the face of the user; yet its recognition effect isliable to be affected by the variations of the ambient lights, to causemarked variations for the characteristic signals obtained throughfetching face images. This in turn could lead to errors in thesubsequent comparisons of the face image signals, and reduced precisionfor the face characteristic comparisons, to adversely affect theaccuracy of face recognition.

The shortcomings mentioned above could be found not only in intelligenthandset, but also in other devices making use of the same facerecognition technology. Yet, in the Prior Art, those shortcomings havenot been effectively overcome.

Therefore, presently, the design and performance of the face and objectrecognition technology is not quite satisfactory, and it leaves muchroom for improvement.

SUMMARY OF THE INVENTION

In view of the problems and drawbacks mentioned above, the presentinvention provides a multi-frequency high-precision object recognitionmethod, to overcome the shortcomings of the Prior Art.

The objective of the present invention is to provide a multi-frequencyhigh-precision object recognition method, comprising the followingsteps:

Providing a recognition hardware mechanism contained in a recognitionsystem, the recognition hardware mechanism having at least amulti-frequency light emitting unit and at least a multi-frequency imagesensor unit.

Irradiating lights of different frequencies emitted by the at least amulti-frequency light emitting unit onto an object-to-be-tested, thelight emitted by the multi-frequency light emitting unit contains atleast two infrared lights, having their wavelength ranges between 850 nmto 1050 nm.

Fetching by the multi-frequency image sensor unit images of theobject-to-be-tested irradiated by lights of different frequencies, suchthat the multi-frequency image sensor unit fetches respective narrowrange image signals contained in at least two reflected infrared lightsrespectively, the wavelength ranges of the narrow range image signalsare between 850 nm to 1050 nm corresponding to that of themulti-frequency light emitting unit, and a wavelength width for each ofthe infrared lights is at least 10 nm to 60 nm.

Locating in an X axis and a Y axis is a single-piece planar image, andin a Z axis is image depths of different wavelengths, wherein a samplewavelength in the Z axis contains at least two infrared light narrowrange image signals, and their wavelength ranges are between 850 nm and1050 nm, corresponding to that of the multi-frequency image sensor unit,the wavelength width for each of the infrared lights is at least 10 nmto 60 nm.

Calculating to obtain a plurality of single-piece planar images in the Xaxis and the Y axis as sampled by different wavelength widths in the Zaxis, superimposing the plurality of single-piece planar images into a3-dimension stereoscopic relief reference image for precise comparisonand recognition.

For the characteristics mentioned above, the present invention can beused in the following applications: security monitoring, industrialmonitoring, human face recognition, image recognition for door openingof a vehicle. In particular, when it is used in an intelligent mobiledevice, it requires less components to function, to save cost and spacesignificantly. In addition, in application, it is able to fetch3-dimension stereoscopic relief images precisely at high speed, withoutbeing affected by the variations of the ambient lights. Therefore, themajor advantage of the present invention is that, it is able to raisethe precision of human face recognition.

In the Prior Art, high price precision elements are used to producespecial effect structured light, to be irradiated onto theobject-to-be-tested. Yet, due to its technical limitations, it may onlyuse ordinary image sensor to receive light, as such it is liable to beaffected by the variations of the ambient lights, to cause inferiorquality of the image produced. Therefore, even if the Structured Lightis used in cooperation with the Time of Flight (TOF) technology in thesubsequent step to produce 3-dimension stereoscopic relief images, theoverall recognition effect is not sufficient, to result in markedreduction of recognition precision.

In contrast, in the present invention, the low price multi-frequencylight emitting units can be used to irradiate flood lights onto theobject-to-be-tested. Then, the multi-frequency image sensor unit capableof producing clear 3-dimension stereoscopic image of front layer andback layer is used, such that the received image having clear frontlayer and back layer is less liable to be affected by the variations ofthe ambient lights. Subsequently, calculate to obtain a plurality ofsingle-piece planar images in the X axis and the Y axis as sampled bydifferent wavelength widths in the Z axis, and superimpose the pluralityof single-piece planar images into a 3-dimension stereoscopic reliefimage for precise comparison and recognition. As such, the recognitioneffects for both the biological and non-biological real entities areimproved significantly, and are much better than that of the Prior Art.

Further scope of the applicability of the present invention will becomeapparent from the detailed descriptions given hereinafter. However, itshould be understood that the detailed descriptions and specificexamples, while indicating preferred embodiments of the presentinvention, are given by way of illustration only, since various changesand modifications within the spirit and scope of the present inventionwill become apparent to those skilled in the art from the detaildescriptions.

BRIEF DESCRIPTION OF THE DRAWINGS

The related drawings in connection with the detailed descriptions of thepresent invention to be made later are described briefly as follows, inwhich:

FIG. 1 is a schematic diagram of a recognition system according to thepresent invention;

FIG. 2 is a schematic diagram of a 3-dimension stereoscopic reliefimages produced by a recognition method according to the presentinvention;

FIG. 3 is a block diagram of a recognition system according to thepresent invention;

FIG. 4 is a schematic diagram of a single-piece multi-frequency imagesensor according to the present invention;

FIG. 5 is a spectrum diagram of image signals received by a single-piecemulti-frequency image sensor according to the present invention;

FIG. 6 is a flowchart of steps of the recognition method for recognizinghuman face according to the present invention;

FIG. 7 is a schematic diagram of a recognition system having an addedambient light sensor according to the present invention;

FIG. 8 is a schematic diagram of a recognition system utilized in anintelligent handset according to the present invention;

FIG. 9 is another spectrum diagram of image signals received by asingle-piece multi-frequency image sensor according to the presentinvention; and

FIG. 10 is a schematic diagram of an Apple iPhone X equipped to performhuman face recognition according to the Prior Art.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The purpose, construction, features, functions and advantages of thepresent invention can be appreciated and understood more thoroughlythrough the following detailed descriptions with reference to theattached drawings.

Refer to FIGS. 1 to 5 respectively for a schematic diagram of arecognition system according to the present invention; a schematicdiagram of a 3-dimension stereoscopic relief images produced by amulti-frequency high-precision object recognition method according tothe present invention; a block diagram of a recognition system accordingto the present invention; a schematic diagram of a single-piecemulti-frequency image sensor according to the present invention; and aspectrum diagram of image signals received by a single-piecemulti-frequency image sensor according to the present invention. Asshown in FIGS. 1 to 5, the multi-frequency high-precision objectrecognition method of the present invention includes the followingsteps:

Providing a recognition hardware mechanism 1 contained in a recognitionsystem 100, with the recognition hardware mechanism 1 having at least amulti-frequency light emitting unit 2 and at least a multi-frequencyimage sensor unit 3.

Irradiating lights of different frequencies emitted by the at least amulti-frequency light emitting unit 2 onto an object-to-be-tested 90,the lights emitted by the multi-frequency light emitting unit 2 containsat least two infrared lights, having their wavelength ranges between 850nm to 1050 nm.

Fetching by the multi-frequency image sensor unit 3 images of theobject-to-be-tested 90 irradiated by lights of different frequencies,such that the multi-frequency image sensor unit 3 fetches respectivenarrow range image signals 301, 302 contained in the at least tworeflected infrared lights respectively, the wavelength ranges of thenarrow range image signals 301, 302 are between 850 nm to 1050 nmcorresponding to that of the multi-frequency light emitting unit 2, anda wavelength width for each of the infrared lights is at least 10 nm to60 nm.

Locating in an X axis and a Y axis is a single-piece planar image, andin a Z axis is image depths of different wavelengths, wherein a samplewavelength in the Z axis contains at least two infrared light narrowrange image signals 301, 302, and their wavelength ranges are between850 nm and 1050 nm, corresponding to that of the multi-frequency imagesensor unit 3, the wavelength width for each of the infrared lights is10 nm to 60 nm.

As shown in FIG. 2, calculating to obtain a plurality of single-pieceplanar images 4 in the X axis and the Y axis as sampled by differentwavelength widths in the Z axis, superimposing the plurality ofsingle-piece planar images 4 into a 3-dimension stereoscopic reliefimage 5 for precise comparison and recognition.

As shown in FIG. 3, the multi-frequency light emitting unit is formed bya plurality of light-emitting-diodes 21 of different frequencies or asingle-piece multi-frequency light-emitting-diode 20, the single-piecemulti-frequency light-emitting-diode 20 emits two infrared lights havingtheir wavelength ranges between 850 nm and 1050 nm. Preferably, the twoinfrared lights are of wavelength 850 nm and 940 nm respectively, oralternatively of wavelength 940 nm and 1050 nm respectively. Also, asshown in FIG. 3, the light-emitting-diodes 21 (1, 2, . . . N), and thesingle-piece multi-frequency light-emitting-diode 20 are connected to alight source controller for switching. While image sensor 31 (1, 2, . .. N), and the single-piece multi-frequency image sensor 30 are connectedto an image processing circuit. The light source controller and theimage processing circuit belong to the Prior Art, and thus they will notbe described in detail herein for brevity.

As shown in FIGS. 3-5, the multi-frequency image sensor unit 3 is formedby a plurality of image sensors 31 of different frequencies or asingle-piece multi-frequency image sensor 30. The single-piecemulti-frequency image sensor 30 includes: a light sensing pixel array310; a packaging circuit 311, and an image enhancing processor unit 312.The a packaging circuit 311 is connected to the light sensing pixelarray 310, to drive the light sensing pixel array 311 to capture outsidelight and convert it into a combined image signal for output, the lightsensing pixel array 310 captures RGB full color visible light, and IRinfrared invisible light to perform photoelectric conversion. The imageenhancing processor unit 312 is embedded in the packaging circuit 311,to control and regulate image captured by the light sensing pixel array310. The image includes: a full color RGB visible light wide range imagesignal 305 having its wavelength range between 400 nm and 700 nm, and atleast two infrared invisible light narrow range image signals 301, 302having their wavelength ranges between 850 nm and 940 nm. The wavelengthwidth for each of the infrared invisible light narrow range imagesignals 301,302 is between 10 nm and 60 nm. The full color RGB visiblelight wide range image signal 305 and the two infrared invisible lightnarrow range image signals 301, 302 are superimposed and combined, toproduce a clear output image having a stereoscopic sense of a frontlayer and a back layer.

Since the combined image signal output is formed by superimposing twoinfrared light narrow range image signals 301,302, having theirwavelength ranges between 850 nm and 1050 nm, so that the recognitioneffect achieved is far better than that of the Prior Art. Also, theclearness and stereoscopic sense of layer are raised. In this way, thesingle-piece multi-frequency image sensor 30 can be used to capture theimages clearly, while being less liable to be affected by the variationsof the ambient lights, to achieve the objective of image recognition.

In the descriptions above, the object-to-be-tested 90 can be a humanface, and that is used quite often in face recognition turn-on of amobile device, or face recognition turn-on of an automatic paymentdevice.

As shown in FIG. 6, in implementing the recognition method of thepresent invention, a preliminary recognition learning unit 6 isprovided, to choose the wavelengths of the two infrared lights emittedby the multi-frequency light emitting unit 2 to be 850 nm and 940 nmrespectively. In step S101, utilizing two infrared lights havingwavelengths 850 nm and 940 nm respectively to each take an image for theupper portion, the lower portion, the center portion, the left portion,and the right portion of the original object 60 (namely, a total of twoimages for each of the portions). In step S102, in the crosstransposition movement of the original object 60, utilizing two infraredlights having frequencies 850 nm and 940 nm respectively to take atleast an image for every other angle (namely, a total of two images forevery other angle). In step S103, calculate to obtain a plurality ofsingle-piece planar images 4 in the X axis and the Y axis as sampled byinfrared lights of different wavelengths of 850 nm and 940 nmrespectively in the Z axis, and superimpose the plurality ofsingle-piece planar images 4 to produce a 3-dimension stereoscopicrelief reference image 65 of the original object 60 for precisecomparison and recognition.

In the descriptions above, in executing a preliminary recognitionlearning unit 6, an interrupted sound or voice can be produced, to serveas a reference indication for angular displacement speeds of theoriginal object 60 moving upward, downward, to the center, to the left,and to the right.

Upon finishing filing the 3-dimension stereoscopic relief referenceimage 65 of the original object 60, in step S201, when the recognitionsystem 100 executes recognition of the object-to-be-tested 90 to obtainthe 3-dimension stereoscopic relief image 5, in step S202, determine ifthe object-to-be-tested 90 is an organic or inorganic real entity. Incase the answer is positive, in step S203, the 3-dimension stereoscopicrelief image 5 is compared with, the 3-dimension stereoscopic reliefreference image 65 of the original object 60 stored in the preliminaryrecognition learning unit 6. Finally, in step S204, in case the formerand the latter are identical, then activate the connection to work,otherwise, the connection is not activated. The technic mentioned abovecan be applied in face recognition turn-on of a handset, facerecognition turn-on of an automatic payment mechanism, or otherapplications in this respect.

As shown in FIG. 7, the recognition system 100 further includes anambient light sensor 70 and an ambient light enhancement comparing unit7. When the ambient light sensor 70 senses that the ambient light is ofa first dim grade, the ambient light enhancement comparing unit 7 isactivated, to compare the 3-dimension stereoscopic relief images 5 ofthe object-to-be-tested 90, with the 3-dimension stereoscopic reliefreference images 65 of the original object 60 fetched by infrared lightof wavelength 940 nm, and when the ambient light sensor senses theambient light is of a second dim grade, then the ambient lightenhancement comparing unit 7 is switched automatically, to compare the3-dimension stereoscopic relief images 5 of the object-to-be-tested 90,with the 3-dimension stereoscopic relief reference images 65 of theoriginal object 60 fetched by infrared light of wavelength 850 nm, andfinally the illumination of the ambient light is adjusted automaticallyto obtain a more precise image recognition.

In the descriptions above, the recognition hardware mechanism 1 can beinstalled on the intelligent mobile device, such as intelligent handset,tablet etc, but the present invention is not limited to this. Inpractice, the recognition hardware mechanism 1 can also be installed ona desk top computer, or a notebook computer. As shown in FIG. 8, inrealizing human face recognition on an intelligent handset, it onlyrequires to use a structure formed by a single-piece multi-frequencylight-emitting-diode 20, a single-piece multi-frequency image sensor 30,and an ambient light sensor 70. Compared with the existing iPhoneXhandset of Apple, The whole outfit thus formed is able to save cost andspace, while the entire recognition precision is raised significantly.

Further, refer to FIG. 3 for a block diagram of a recognition systemaccording to the present invention. As shown in FIG. 3, in order toobtain more precise recognition, in addition to the face recognitionmentioned above, eye iris can be added to enhance precision ofrecognition. In this approach, two biological characteristics arecombined to provide higher degree of recognition precision.

As shown in FIGS. 4 and 5, in the present invention, in order to achievebetter recognition, the image signal formed by superimposing the widerange image signal 305 and at least two narrow range image signals 301,302 is used, to realize clearness of layers and to give a sense of depthand layer. And this can be used to calculate precisely the 3-dimensioncharacteristics of the object-to-be-tested 90, such as distance ofdepth, gesture of hand, getting around obstacle, etc. That is quiteimportant for 3-dimension image depth and distance measurementapplications, such as Virtual Reality/Augmented Reality (VR/AR), drone,people/things counting. Further, it is capable of performing depthmeasurements for object-to-be-tested 90 and its surroundings. As such,the technology of the present invention can also be used in the fieldsof Artificial Intelligence (AI), and Computer Vision. For example, therecognition hardware mechanism 1 can be installed in a vehicle, and isused for face recognition door opening for an automobile, or fatiguedetection for a motor cyclist, but the present invention is not limitedto this.

Moreover, as shown in FIGS. 4 and 9, in the present invention, the imageenhancing processor unit 312 can be realized through a software or afirmware, to facilitate revising or increasing the amount of the narrowrange image signals captured, or adjusting the transmittance of theimage signal to a range between 30% and 95%. For example, in imagefetching, an additional narrow range image signal 303 of wavelength 1050nm can be added. As such, through superimposing three narrow range imagesignals of infrared light of wavelengths 850 nm, 940 nm, 1050 nm, therecognition depth and sense of layer can be more evident, to raiseeffectively the clearness and stereoscopic sense of the image.

In the descriptions above, one narrow range image signal is added,however, the present invention is not limited to this. In fact, theamount of narrow range image signals added can be classified intovarious grades corresponding to different recognition precisions. Assuch, it can be customized to recognize the object-to-be-tested 90 as abiological or a non-biological real entity, and be used extensively invarious applications, such as security monitoring, industrialmonitoring, face recognition, webcam, drone, robot, and vehicle backupauxiliary image fetching.

The above detailed description of the preferred embodiment is intendedto describe more clearly the characteristics and spirit of the presentinvention. However, the preferred embodiments disclosed above are notintended to be any restrictions to the scope of the present invention.Conversely, its purpose is to include the various changes and equivalentarrangements which are within the scope of the appended claims.

What is claimed is:
 1. A multi-frequency high-precision objectrecognition method, comprising the following steps: providing arecognition hardware mechanism contained in a recognition system, therecognition hardware mechanism having at least a multi-frequency lightemitting unit and at least a multi-frequency image sensor unit;irradiating lights of different frequencies emitted by the at least amulti-frequency light emitting unit onto an object-to-be-tested, thelights emitted by the multi-frequency light emitting unit contains atleast two infrared lights, having their wavelength ranges between 850 nmto 1050 nm; fetching by the multi-frequency image sensor unit images ofthe object-to-be-tested irradiated by lights of different frequencies,such that the multi-frequency image sensor unit fetches respectivenarrow range image signals contained in the at least two reflectedinfrared lights respectively, the wavelength ranges of the narrow rangeimage signals are between 850 nm to 1050 nm corresponding to that of themulti-frequency light emitting unit, and a wavelength width for each ofthe infrared lights is at least 10 nm to 60 nm; locating in an X axisand a Y axis is a single-piece planar image, and in a Z axis is imagedepths of different wavelengths, wherein a sample wavelength in the Zaxis contains at least two infrared light narrow range image signals,and their wavelength ranges are between 850 nm and 1050 nm,corresponding to that of the multi-frequency image sensor unit, thewavelength width for each of the infrared lights is at least 10 nm to 60nm; and calculating to obtain a plurality of single-piece planar imagesin the X axis and the Y axis as sampled by different wavelength widthsin the Z axis, superimposing the plurality of single-piece planar imagesinto a 3-dimension stereoscopic relief image for precise comparison andrecognition.
 2. The multi-frequency high-precision object recognitionmethod as claimed in claim 1, wherein the multi-frequency image sensorunit is formed by a plurality of image sensors of different frequenciesor a single-piece multi-frequency image sensor, the single-piecemulti-frequency image sensor includes: a light sensing pixel array; apackaging circuit, connected to the light sensing pixel array, to drivethe light sensing pixel array to capture outside light and convert itinto a combined image signal for output, the light sensing pixel arraycaptures RGB full color visible light, and IR infrared invisible lightto perform photoelectric conversion; and an image enhancing processorunit, embedded in the packaging circuit, to control and regulate imagecaptured by the light sensing pixel array, the image includes: a fullcolor RGB visible light wide range image signal having its wavelengthrange between 400 nm and 700 nm, and at least two infrared invisiblelight narrow range image signals and having their wavelength rangesbetween 850 nm and 940 nm, a wavelength width for each of the twoinfrared invisible light narrow range image signals is between 10 nm and60 nm, the full color RGB visible light wide range image signal and thetwo infrared invisible light narrow range image signals are superimposedand combined, to produce a clear output image having a stereoscopicsense of a front layer and a back layer.
 3. The multi-frequencyhigh-precision object recognition method as claimed in claim 1, whereinthe multi-frequency light emitting unit is formed by a plurality oflight-emitting-diodes of different frequencies or a single-piecemulti-frequency light-emitting-diode, the single-piece multi-frequencylight-emitting-diode emits at least two infrared lights with theirwavelength ranges between 850 nm and 1050 nm.
 4. The multi-frequencyhigh-precision object recognition method as claimed in claim 1, whereinthe object-to-be-tested is a biological or non-biological real entity, apreliminary recognition learning unit is provided, to utilize the twoinfrared light narrow range image signals with wavelength of 850 nm and940 nm respectively, to take at least an image for an upper portion, alower portion, a central portion, a left portion, and a right portionrespectively of an original object, and to take at least an image of theoriginal object when it moves in every other angle in crosstransposition, calculate to obtain a plurality of single-piece planarimages in the X axis and the Y axis, as sampled by infrared lights ofdifferent wavelengths of 850 nm and 940 nm respectively in the Z axis,and superimpose the plurality of single-piece planar images into a3-dimension stereoscopic relief reference image for subsequentcomparison and recognition.
 5. The multi-frequency high-precision objectrecognition method as claimed in claim 4, wherein in executing apreliminary recognition learning unit, an interrupted sound or voice isproduced, to serve as a reference indication for angular displacementspeeds of the original object moving upward, downward, to the center, tothe left, and to the right.
 6. The multi-frequency high-precision objectrecognition method as claimed in claim 4, wherein in completing the3-dimension stereoscopic relief images of the object-to-be-tested,firstly, determine if the object-to-be-tested is a real entity, in caseit is, compare the 3-dimension stereoscopic relief images of theobject-to-be-tested, with the 3-dimension stereoscopic relief referenceimage of the original object stored in the preliminary recognitionlearning unit, to determine if they are identical, in case it isidentical, activate connection to work, otherwise not activate theconnection.
 7. The multi-frequency high-precision object recognitionmethod as claimed in claim 4, wherein the recognition hardware mechanismfurther includes an ambient light sensor, and a corresponding ambientlight enhancement comparing unit is provided, when the ambient lightsensor senses the ambient light is of a first dim grade, the ambientlight enhancement comparing unit is activated, to compare the3-dimension stereoscopic relief images of the object-to-be-tested, withthe 3-dimension stereoscopic relief reference images of the originalobject fetched by infrared light of wavelength 940 nm, and when theambient light sensor senses the ambient light is of a second dim grade,then the ambient light enhancement comparing unit is switchedautomatically, to compare the 3-dimension stereoscopic relief images ofthe object-to-be-tested, with the 3-dimension stereoscopic reliefreference images of the original object fetched by infrared light ofwavelength 850 nm, and finally the illumination of the ambient light isadjusted automatically to achieve a precise image recognition.
 8. Themulti-frequency high-precision object recognition method as claimed inclaim 1, wherein the object-to-be-tested is a human face.
 9. Themulti-frequency high-precision object recognition method as claimed inclaim 1, wherein the object-to-be-tested is a human face or a human eyeiris.
 10. The multi-frequency high-precision object recognition methodas claimed in claim 1, wherein the recognition hardware mechanism isinstalled on an intelligent mobile device.
 11. The multi-frequencyhigh-precision object recognition method as claimed in claim 1, whereinthe recognition hardware mechanism is installed on a vehicle.